DGFE-Mamba: Mamba-Based 2D Image Segmentation Network

Abstract

In the field of medical image processing, combining global and local relationship modeling constitutes an effective strategy for precise segmentation. Prior research has established the validity of Convolutional Neural Networks (CNN) in modeling local relationships. Conversely, Transformers have demonstrated their capability to effectively capture global contextual information. However, when utilized to address CNNs’ limitations in modeling global relationships, Transformers are hindered by substantial computational complexity. To address this issue, we introduce Mamba, a State-Space Model (SSM) that exhibits exceptional proficiency in modeling long-range dependencies in sequential data. Given Mamba’s demonstrated potential in 2D medical image segmentation in previous studies, we have designed a Dual-encoder Global-local Feature Extraction Network based on Mamba, termed DGFE-Mamba, to accurately capture and fuse long-range dependencies and local dependencies within multi-scale features. Compared to Transformer-based methods, the DGFE-Mamba model excels in comprehensive feature modeling and demonstrates significantly improved segmentation accuracy. To validate the effectiveness and practicality of DGFE-Mamba, we conducted tests on the Automatic Cardiac Diagnosis Challenge (ACDC) dataset, the Synapse multi-organ CT abdominal segmentation dataset, and the Colorectal Cancer Clinic (CVC-ClinicDB) dataset. The results showed that DGFE-Mamba achieved Dice coefficients of 92.20, 83.67, and 94.13, respectively. These findings comprehensively validate the effectiveness and practicality of the proposed DGFE-Mamba architecture.